SERS-Integrated Microneedles: Bridging Nanoplasmonics and Microsampling for Advanced Bioanalysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sensitive analytical techniques capable of in situ measurements in biological tissues with high selectivity and rapid response are essential for health monitoring, disease diagnosis, agriculture management, and food safety. However, conventional biological sampling is often invasive, expensive, and inconvenient. Microneedle (MN) technology offers a noninvasive, quick, and self-administered approach for in vivo sampling of extracellular fluids that are rich in biomarkers and metabolites indicative of health status. By integrating MNs with highly sensitive surface-enhanced Raman spectroscopy (SERS), the hybrid technique provides unprecedented convenience, user compliance, and analytical sensitivity for biomonitoring. The versatility of SERS-integrated MNs (SERS-MNs), along with their integration into portable, self-administered devices, makes them ideal for point-of-care testing. SERS-MNs can also be incorporated into wearable medical devices for real-time, long-term biochemical monitoring with high temporal resolution. This perspective explores the emerging applications of SERS-MNs by critically examining the key requirements in materials, structural design, and fabrication methods, while elucidating their underlying working principles. We further assess current challenges and highlight future opportunities, providing insights to advance their use in clinical diagnostics, precision agriculture, and food safety. This work offers a systematic discussion on the integration of SERS-MNs into wearable devices for long-term, real-time health monitoring, opening new possibilities to empower individuals in proactive health management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it